2 citations · 3 across the 4 of their papers we have counts for
4 papers
InfoDecom: Decomposing Information for Defending Against Privacy Leakage in Split Inference
Ruijun Deng, Zhihui Lu, Qiang Duan
Split inference (SI) enables users to access deep learning (DL) services without directly transmitting raw data. However, recent studies reveal that data reconstruction attacks (DR…
Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis
Ruijun Deng, Zhihui Lu, Qiang Duan +1
Split inference (SI) partitions deep neural networks into distributed sub-models, enabling collaborative learning without directly sharing raw data. However, SI remains vulnerable…
Backdoor Attack on Vertical Federated Graph Neural Network Learning
Jirui Yang, Peng Chen, Zhihui Lu +3
Federated Graph Neural Network (FedGNN) integrate federated learning (FL) with graph neural networks (GNNs) to enable privacy-preserving training on distributed graph data. Vertica…
LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Embodied Intelligence System
Shijing Hu, Zhihui Lu, Xin Xu +3
Embodied intelligence (EI) enables manufacturing systems to flexibly perceive, reason, adapt, and operate within dynamic shop floor environments. In smart manufacturing, a represen…